unit economics

A principal-level design and strategy question. Every default assumption (literacy, a good phone, fast data, English, a credit card) fails. The answer has to pick a job worth solving and an economic model that survives.

A judgment call with no default answer. Principal-level candidates segment the users, price the value, check the strategic role, and make a decision with a dated review rather than a vague 'improve it'.

Speaking is the skill learners avoid and value most. Design short, scenario-based calls at the learner's level with one correction per turn, and move it into the main paid plan once the cost per minute allows.

AI changes pricing because each use costs money and value per use varies. The interviewer wants the trade-offs of each model, matched to the product, and a hybrid that protects margin.

A market-entry question where copying the US playbook fails. The interviewer wants segments, languages, pricing that works with inference costs, distribution and the competitive landscape.

A pricing question about where the value of falling model costs should go. The answer depends on margin targets, competition and whether price or capability wins customers.

Matching free is tempting and often ruinous when every use costs money. The interviewer wants a segmented response that protects the customers you can keep, the margin you need, and the value only you deliver.

Pay for content that measurably improves answers, in the form that matches how you use it: per use for grounding, flat licences for unique training data, and nothing for sources that don't move quality.

Figma reported exactly this drop, driven by AI hosting costs, and moved to enforce credit limits. Find which features and customers cost the margin, meter heavy use, and keep margin only where AI does not buy retention.

Two Indian telecom deals put AI subscriptions in front of hundreds of millions. Take it only with a cost cap, success measured on active users, and a billing and consent handoff agreed at signing.

Default placement buys reach, not users. Pay for activated users rather than shipped devices, refuse exclusivity, and gate renewal on activation and retention.

A puzzle with a precise answer. Price per token is only comparable if a token means the same thing, and across model generations it often does not.

Sending the whole video with every question costs about a quarter of a million tokens. Index it once, retrieve the right minutes and keep the whole-video pass for one-off questions.

The model only knows what it was trained on. Internal policy belongs in your own dated retrieval, public news in search, and only the questions that need it should pay for search.

Hundreds of examples in a cached prompt can match fine-tuning and let you change behaviour in minutes. Price both, then decide by volume, latency and how often the examples change.

At a billion requests a day, a fraction of a cent per call is millions of dollars a month and every millisecond is capacity. Principal answers start from the arithmetic and reshape the feature, the models and the serving to fit.

The model is one box. Prompt and output filters, brand and rights rules, provenance labels, cost per kept image and a queue for peak hours decide whether the feature works.

Twenty thousand calls a day, two languages and a telephone network you do not control. Scope the intents, budget every millisecond and price the handoff before you promise containment.

The token bill drops by two-thirds and failures rise by one point. Price the failures before you call it a saving, and check what routing does to your cache.

Tokens cost under a dollar per run; reviewer time costs thirty times that. Choose the task classes, the permissions and the gates that decide what a human ever sees.

A range is not a pricing input. Work out cost per accepted task by effort level and cost per account by cohort, compare flat, allowance and usage plans on the same customers, then say what would change your choice.

An estimation question that tests commercial judgment. The inference bill is where the arithmetic starts; the number that decides the answer is somewhere else.

A vocabulary question with a sharp edge. The cheapest model per token is regularly the most expensive per finished job.

The cheap option per conversation is the expensive one per customer, until you read how the vendor defines 'resolved'. Two calculations, and the second one is where the offer is decided.

Candidates jump to 'switch to a cheaper model'. The interviewer wants to see you find out why cost per user rose before choosing a lever, then pull the levers in the order that protects quality.

A margin question with a constraint added halfway through. The average user is not the problem; the distribution is, and a flat price hides it until the model gets more expensive.

Routing is one of the biggest cost levers in AI products, and one of the easiest to get subtly wrong. The interviewer wants the savings arithmetic and the misrouting risk in the same answer.

Caching is the cheapest large saving in most AI products, and a single line of personalisation at the top of the prompt can turn it into a 22% cost increase. The arithmetic is short; the layout insight is what scores.

A principal-level question because the cost of serving a user is the same in Mumbai and Minneapolis while the price you can charge is not. The free tier is an acquisition budget, and the paid tier needs a model mix by market.

A portfolio and stakeholder question. Across-the-board cuts are the easy answer and the wrong one. The strong answer finds where spend creates value, cuts waste first, and gives the CFO a number they can hold you to.

When the metric is the invoice, every edge case becomes a billing dispute. Principal-level answers design a definition customers can audit and a price that survives the worst month.

Batch pricing halves the bill on eligible work, and the real product question is what you can precompute before anyone asks for it.

Turn a registered-user count into active usage, model calls and billable tokens. Compare an uncached baseline with a stated caching scenario without inventing a vendor quote.

Cheaper tokens bring longer prompts, more steps and more tasks, and some prices rise. Forecast spend as price times tokens per task times tasks times users, in bands, with caps per feature.

Industry data shows AI apps earn more per payer and churn faster. Compute lifetime value net of serving cost, find which jobs are one-off, and either build a recurring loop or price for the one-off job.

The spreadsheet comparison everyone gets wrong by assuming full GPU utilisation and forgetting the team. The interviewer wants a break-even with honest utilisation, operating costs and a quality check.

API pricing shapes which products developers can build. The interviewer wants tier spread, input and output pricing, discounts that reward efficient usage, enterprise commitments, and predictability.

A working prototype is the start of the decision, not the end. The interviewer wants evidence on value, feasibility at real quality, cost, risk and strategic fit, and a decision rule set before the evidence arrives.

AI costs vary by request, so pricing has to change, and people judge changes by fairness. Show the cost case, the communication and how you made it right when it landed badly.